# Purchase Test Cohort Attribution Detector

> Purchase Test Cohort Attribution Detector is a paid API for AI agents from detect.halowerk.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-11-04).

Separates internal acceptance tests, external real usage, and contradictory assignments within an anonymous purchase ledger cohort dataset.

## Facts

- Endpoint: POST https://detect.halowerk.com/v1/F-N-PURCHASE-COHORTS/purchase-test-cohort-attribution?utm_source=zero.xyz
- Price: $0.003/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-04
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/purchase-test-cohort-attribution-detector-bc7299a4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7pN22Hi4c2o_sQI9Ck7na

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability purchase-test-cohort-attribution-detector-bc7299a4 -d '<json body>'
```

Example prompt: I have a batch of anonymous purchase cohort events — 50 event arrays — and I'm claiming external net abstract units of 1200. Can you run them through the purchase test cohort attribution detector to separate out which ones are internal acceptance tests, which are real external usage, and flag any contradictory attributions?

## When to prefer this

Use this endpoint when you have an anonymous purchase event ledger and need to programmatically distinguish internal QA/acceptance test traffic from genuine external customer usage, especially when cohort attributions may be inconsistent or contradictory. Prefer this over manual filtering or generic anomaly detection when your data conforms to the cohort-event-array format with a scalar claim.

## Known failure modes

- Event arrays exceeding 128 items or 128 elements per array will be rejected with a validation error
- Claim values outside the range -1,000,000 to 1,000,000 will fail schema validation
- Malformed or missing purchase_cohorts object returns a 400 bad request
- Empty event arrays violate minItems constraint and will error
- Payment not provided or insufficient USDC balance returns 402 Payment Required

## How this service works

Trennt in einem gelieferten anonymen Kaufledger interne Abnahmetests, externe Nutzung und widersprüchliche Zuordnungen.

## Output

Returns a structured breakdown of the submitted purchase cohort events, classifying each into internal acceptance tests, genuine external usage, or contradictory/conflicting attributions, along with validation of the supplied scalar claim against the computed external net abstract units (within 1e-9 tolerance).

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "artifact": {
   "type": "object",
   "required": [
    "purchase_cohorts"
   ],
   "properties": {
    "purchase_cohorts": {
     "type": "object",
     "required": [
      "events",
      "claim"
     ],
     "properties": {
      "claim": {
       "type": "number",
       "maximum": 1000000,
       "minimum": -1000000,
       "description": "Explicit supplied scalar claim for external_net_abstract_units; 1e-9 absolute/relative correspondence tolerance."
      },
      "events": {
       "type": "array",
       "items": {
        "type": "array",
        "items": {
         "type": "number",
         "maximum": 1000000,
         "minimum": -1000000
        },
        "maxItems": 128,
        "minItems": 1
       },
       "maxItems": 128,
       "minItems": 1
      }
     },
     "additionalProperties": false
    }
   },
   "additionalProperties": false
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/purchase-test-cohort-attribution-detector-bc7299a4/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from detect.halowerk.com](https://www.zero.xyz/host/detect.halowerk.com/llms.txt)